A customisable downscaling approach for local-scale meteorological and air pollution forecasting: Performance evaluation for a year of urban meteorological forecasts

2010 ◽  
Vol 25 (1) ◽  
pp. 82-92 ◽  
Author(s):  
M. Thatcher ◽  
P. Hurley
Keyword(s):  
Air Pollution ◽  
Performance Evaluation ◽  
Local Scale ◽  
Forecasting Performance ◽  
Air Pollution Forecasting

scholarly journals Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis

2014 ◽  
Vol 5 (4) ◽  
pp. 696-708 ◽  
Author(s):  
Madhavi Anushka Elangasinghe ◽  
Naresh Singhal ◽  
Kim N. Dirks ◽  
Jennifer A. Salmond
Keyword(s):  
Air Pollution ◽  
Sensitivity Analysis ◽  
Explicit Knowledge ◽  
Forecasting System ◽  
Air Pollution Forecasting

scholarly journals Peramalan Pencemaran Udara Di Kota Surabaya Menggunakan Metode DSARIMA dengan Pendekatan Percentile Error Bootstrap (PEB)

2021 ◽  
Vol 8 (5) ◽  
pp. 987
Author(s):  
Novi Koesoemaningroem ◽  
Endroyono Endroyono ◽  
Supeno Mardi Susiki Nugroho
Keyword(s):  
Air Pollution ◽  
Actual Data ◽  
Forecast Data ◽  
Accuracy Result ◽  
The Difference ◽  
The Impact ◽  
Air Pollution Forecasting

<p>Peramalan pencemaran udara yang  akurat  diperlukan untuk mengurangi dampak pencemaran udara. Peramalan yang belum akurat akan berdampak kurang efektifnya tindakan yang dilakukan untuk mengantisipasi dampak pencemaran udara. Sehingga diperlukan sebuah pendekatan yang dapat mengetahui keakuratan plot data hasil peramalan. Penelitian ini dilakukan dengan tujuan melakukan peramalan pencemaran udara berdasarkan parameter PM<sub>10</sub>, NO<sub>2</sub>, CO, SO<sub>2</sub>, dan O<sub>3</sub>dengan metode DSARIMA. Data dalam penelitian ini sebanyak 8.760 data yang berasal dari Dinas Lingkungan Hidup Kota Surabaya. Berdasarkan hasil peramalan selama 168 jam kadar parameter PM<sub>10</sub>, NO<sub>2</sub>, SO<sub>2</sub> dan O<sub>3</sub> cenderung  menurun. Hasil peramalan selama 168 jam dengan menggunakan DSARIMA memberikan hasil peramalan yang nilainya mendekati data aktual terbukti dari polanya yang sesuai atau mirip dengan grafik plot data aktual dengan hasil ramalan. Dengan pendekatan PEB, selisih antara data aktual dan data ramalan kecil dan plot grafik PEB mengikuti plot grafik di data aktual, sehingga dapat dikatakan bahwa model sudah sesuai. Hasil akurasi terbaik yang dihasilkan adalah model DSARIMA dengan RMSE terkecil 0,59 didapatkan dari parameter CO yaitu ARIMA(0,1,[1,2,3])(0,1,1)<sup>24</sup>(0,1,1)<sup>168</sup>.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Accurate air pollution forecasting is needed to reduce the impact of air pollution. Inaccurate forecasting will result in less effective actions taken to anticipate the impact of air pollution. So we need an approach that can determine the accuracy of the forecast data plot. This research was conducted with the aim of forecasting air pollution based on the PM<sub>10</sub>, NO<sub>2</sub>, CO, <sub>SO2</sub>, and O<sub>3</sub> parameters using the DSARIMA method. The data in this study were 8.760 data from the Surabaya City Environmental Service. Based on the results of forecasting for 168 hours, the levels of PM<sub>10</sub>, NO<sub>2, </sub>SO<sub>2</sub>, and O<sub>3</sub> parameters tend to decrease. Forecasting results for 168 hours using DSARIMA provide forecasting results whose values are close to the actual data as evidenced by the pattern that matches or is similar to the actual data plot graph with the forecast results. With the PEB approach, the difference between the actual data and the forecast data is small and the PEB graph plot follows the graph plot in the actual data, so it can be said that the model is appropriate. The best accuracy result is DSARIMA with the smallest RMSE 0,59 obtained from the CO parameter, namely </em>ARIMA(0,1,[1,2,3])(0,1,1)<sup>24</sup>(0,1,1)<sup>168</sup>.</p><p> </p><p> </p>

Investigation of factors influencing employee performance

2016 ◽  
Vol 24 (2) ◽  
pp. 340-368 ◽  
Author(s):  
Jiří Šindelář
Keyword(s):  
Performance Evaluation ◽  
Work Experience ◽  
Formal Education ◽  
Employee Performance ◽  
Organizational Factors ◽  
Personal Interview ◽  
Content Type ◽  
Organizational Environments ◽  
Forecasting Performance ◽  
Soft Factors

Purpose The purpose of this paper is to investigate the effect of selected organizational factors on the performance of employees charged with sales forecasting, and to compare this across the different organizational environments of Central-Eastern European (CEE) retail chains. Design/methodology/approach The research involves seven major pan-European retail chain companies, with a total number of 201 respondents. Data were collected via a questionnaire [computer-aided personal interview (CAPI) and human-aided personal interview (HAPI) method] with a five-point scale evaluation of both dependent (organizational factors) and independent (performance indicator) variables. Cluster analysis was then used to derive the characteristics of average organizational environments, and correlation analysis was used to investigate the direction and size of the performance effect. Findings The results confirmed that different organizational environments have differing effects on the performance of forecasters. It also showed that the “hard core” factors (performance evaluation and information systems) do not have a dominant effect on employee performance in any of the environments regardless of their quality, and are aggregately outclassed by “soft” factors (communication lines and management support). Finally, the research indicated that among the personal attributes related to individual forecasters, domain and forecasting work experience have significant, beneficial effects on forecasting performance, whereas formal education level was detected to have a negative effect and can be, at best, considered as non-contributor. Practical implications The research results along with available literature enable us to define four management theses (focus on system, less on people; soft factors are equal to hard ones; higher formal education does not contribute to forecasting performance; and do not overestimate the social and morale situation on the working place) as well as four stages of organizational development, creating a practitioner’s guide to necessary steps to improve an environment’s key factors, i.e. performance evaluation, information systems and forecasting work experience. Originality/value Although there are regular studies examining the effect of organizational factors on employee performance, very few have explored this relationship in a forecasting context, i.e. in the case of employees charged with sales forecasting. Furthermore, the paper brings evidence on this topic from the CEE area, which is not covered in most prominent forecasting management studies.

Air pollution forecasting based on attention‐based LSTM neural network and ensemble learning

2019 ◽  
Vol 37 (3) ◽  
Author(s):  
Duen‐Ren Liu ◽  
Shin‐Jye Lee ◽  
Yang Huang ◽  
Chien‐Ju Chiu
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Ensemble Learning ◽  
Air Pollution Forecasting
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